import pandas as pd
from sklearn.cluster import KMeans
from sklearn.preprocessing import StandardScaler
import matplotlib.pyplot as plt
import seaborn as sns

import warnings
warnings.filterwarnings("ignore")

# 加载数据
df = pd.read_csv('./scenic_data.csv')

# 提取特征并标准化
features = df[['non_weekend_ratio', 'out_province_retio', 'elderly_retio']]
scaler = StandardScaler()
scaled_features = scaler.fit_transform(features)

# 使用k-means 聚类
kmeans = KMeans(n_clusters=4, random_state=42)
df['cluster'] = kmeans.fit_predict(scaled_features)

# 查看聚类结果
print(df[['tourist_agency_name', 'cluster']])

# 查看聚类中心（反标准化）
centers = scaler.inverse_transform(kmeans.cluster_centers_)
print(pd.DataFrame(centers, columns=['non_weekend_ratio', 'out_province_retio', 'elderly_retio']))

# 保存结果
df.to_csv('clustered_agencies.csv', index=False)
# 将聚类中心转换为DataFrame
centers_df = pd.DataFrame(centers, columns=features.columns)
centers_df['cluster'] = [f'Cluster {i}' for i in range(centers.shape[0])]

# 将宽边转换为长表（适合Seaborn)
centers_long = centers_df.melt(id_vars='cluster', var_name='feature', value_name='value')

# 绘制分组柱状图
colors = ['#1f77b4', '#ff7f0e', '#2ca02c'] # 蓝 橙 绿
plt.figure(figsize=(10, 6))
sns.barplot(x='feature', y='value', hue='cluster', data=centers_long, palette=colors)
plt.title('聚类中心特征对比')
plt.ylabel('比例')
plt.ylim(0, 1)
plt.legend(title='簇', bbox_to_anchor=(1.05, 1),loc='upper left')

# 添加数值标签
for p in plt.gca().patches:
    height = p.get_height()
    plt.gca().text(p.get_x() + p.get_width() / 2, height + 0.02,
                   f'{height: .2f}', ha='center')
    
plt.tight_layout()
plt.show()